Multiscale Geometric Analysis Tool Research Articles

Research on real-time data transmission and multi-scale video image decomposition of embedded optical sensor array based on machine learning

Aiming at the research of real-time data transmission and multi-scale image decomposition of embedded optical sensor array, the principle, method and fusion strategy of multi-sensor image fusion are studied comprehensively, thoroughly and systematically by combining the imaging characteristics of source image with multi-scale geometric analysis tools using machine learning algorithm. A new quality scalable video image coding framework is also proposed in this paper, which is implemented by a multi-scale online dictionary learning algorithm based on structured sparse video signals. For the purpose of different types of images and image fusion, a new high quality scalable video image coding framework based on machine learning algorithm is proposed on the basis of comprehensive analysis of prior information such as imaging mechanism of image sensor and imaging characteristics of source image. A multi-scale online dictionary learning algorithm based on machine learning for sparse video signal structure is proposed. Through the hierarchical structure of wavelet decomposition, the searching domain of online learning is optimized to a hierarchical sparse block, and its sparse representation coefficients are obtained by using machine learning sparse coding idea. The real-time data transmission of embedded optical sensor array based on machine learning and multi-scale image decomposition algorithm proposed in this paper have good fusion performance, which is of great significance for further research and engineering application of image fusion technology.

Road Detection Based on Shearlet for GF-3 Synthetic Aperture Radar Images

GF-3 satellite is China’s first C-band multi-polarized synthetic aperture radar (SAR) satellite with the 1-meter resolution, which has been widely used in various fields. Road detection for GF-3 SAR images is an important part of the application of GF-3, especially in fields of map update, target recognition, and image matching. However, speckle appears in GF-3 SAR images due to coherent imaging system and it hinders the interpretation of images seriously. Especially the detection of weak roads under strong speckle background becomes extremely difficult. As a representative of multiscale geometric analysis (MGA) tool, shearlet has the optimal sparse representation feature and strong directional orientation, which can effectively capture edge and other anisotropic feature information, and can accurately describe the sparse characteristics of GF-3 SAR images. Based on shearlet, a method for detecting weak roads under strong speckle interference is proposed. Firstly, the Frost filter is used for despeckling. Secondly, shearlet is used for road detection. Finally, morphological operations are adopted to obtain the final result. Road detection experiments on various types of GF-3 SAR images demonstrate that, the proposed method can effectively overcome the interference of speckle, and completely and smoothly detect road information, which is very suitable for the detection of weak roads under strong speckle interference of GF-3 SAR images.

An image NSCT-HMT model based on copula entropy multivariate Gaussian scale mixtures

As an important multiscale geometric analysis tool, the nonsubsampled contourlet transform (NSCT) has a strong ability in capturing anisotropy and directional features of images. This paper proposes a copula entropy multivariate Gaussian scale mixtures based nonsubsampled contourlet transform hidden Markov tree model (CE-NSCT-HMT). First, we study the probability density distribution of the NSCT coefficients and observe that they exhibit a sharp peak at the zero amplitude and two heavy tails on both sides of the peak. Second, we redefine the generalized neighborhood relationship of the NSCT coefficients and then analyze the joint statistical property among them. We get that the NSCT coefficients have the strongest correlation with their newly defined ‘four neighborhood’ coefficients. Third, we use the Gaussian copula function to model the NSCT coefficients with their ‘four neighborhood’ coefficients. Based on the copula entropy value, the copula entropy multivariate Gaussian scale mixtures distribution is proposed, the application of copula entropy helps the contourlet a lot in localizing textures. In addition, we combine these studies with the hidden Markov tree model and propose the CE-NSCT-HMT model. Finally, the proposed model is applied to image denoising and achieve a good performance. The copula entropy is first applied to the correlation measurement of multiscale decomposition coefficients in this paper, which provides a way to further extend the application of copula entropy to other image processing area.

A Hybrid Curvelet Transform and Genetic Algorithm for Image Steganography

In this paper, we present a new hybrid image steganography algorithm by combining two famous techniques which are curvelet transform and genetic algorithm GA. The proposed algorithm is called Hybrid Curvelet Transform and Genetic Algorithm for image steganography (HCTGA). Curvelet transform is a multiscale geometric analysis tool, its main advantage is that it can solve the important problems efficiently and other transforms are not that ideal. Genetic algorithm is a famous optimization algorithm with the aim of finding the best solutions to a given computational problem that maximizes or minimizes a particular function. In the proposed algorithm the cover and secret images are passed through a preprocessing process by applying four different filters to them in order to remove the noise and achieve a better quality to both images before the hiding process. Then the curvelet transform is applied to the cover image to find the curvelet frequencies of the image, and the secret image is hided at the Least Significant Bits (LSB) of the curvelet frequencies of the cover image to reconstruct the stego image. Finally genetic algorithm operations are employed to find different scenarios for the hiding process by rearranging the hiding bits and finally choose the best scenario that can reach a better image quality and a higher Peak Signal to Noise Ratio (PSNR) results.

Medical Image Fusion Based on Feature Extraction and Sparse Representation.

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

Speckle reduction in optical coherence tomography images based on wave atoms.

Optical coherence tomography (OCT) is an emerging noninvasive imaging technique, which is based on low-coherence interferometry. OCT images suffer from speckle noise, which reduces image contrast. A shrinkage filter based on wave atoms transform is proposed for speckle reduction in OCT images. Wave atoms transform is a new multiscale geometric analysis tool that offers sparser expansion and better representation for images containing oscillatory patterns and textures than other traditional transforms, such as wavelet and curvelet transforms. Cycle spinning-based technology is introduced to avoid visual artifacts, such as Gibbs-like phenomenon, and to develop a translation invariant wave atoms denoising scheme. The speckle suppression degree in the denoised images is controlled by an adjustable parameter that determines the threshold in the wave atoms domain. The experimental results show that the proposed method can effectively remove the speckle noise and improve the OCT image quality. The signal-to-noise ratio, contrast-to-noise ratio, average equivalent number of looks, and cross-correlation (XCOR) values are obtained, and the results are also compared with the wavelet and curvelet thresholding techniques.

On the Splitting Algorithm Based on Multi-target Model for Image Segmentation

Against to the different regions of membership functions indicated image in the traditional image segmentation variational model, resulting segmentation is not clear, de-noising effect is not obvious problems, this paper proposes multi-target model for image segmentation and the splitting algorithm. The model uses a sparse regularization method to maintain the boundaries of segmented regions, to overcome the disadvantages of segmentation fuzzy boundaries resulting from total variation regularization. The algorithm uses the multi-scale geometric analysis tool to maintain the geometry of image, which multi-scale geometric transformations can be flexibly selected according to actual problems. Experimental results show that: the proposed model and the algorithm are feasible and effective.

An Image Fusion Method Based on NSCT and Dual-channel PCNN Model

NSCT is one of useful multiscale geometric analysis tools, which takes full advantage of geometric regularity of image intrinsic structures. The dual-channel PCNN is a simplified PCNN model, which can process multiple images by a single PCNN. This saves time in the process of image fusion and cuts down computational complexity. In this paper, we present a new image fusion scheme based on NSCT and dual-channel PCNN. Firstly, the fusion rules of subband coefficients of NSCT are discussed. For the fusion rule of low frequency coefficients, the maximum selection rule (MSR) is used. Then, for the fusion rule of high frequency coefficients, spatial frequency (SF) of each high frequency subband is considered as the gradient features of images to motivate dual-channel PCNN networks and generate pulse of neurons. At last, fused image is obtained by using the inverse NSCT transform. In order to show that the proposed method can deal with image fusion, we used two pairs of images as our experimental subjects. The proposed method is compared with other five methods. The performance of various methods is mathematically evaluated by using four image quality evaluation criteria. Experimental comparisons conducted on different fusion methods prove the effectiveness of the proposed fusion method

Multi-focus Image Fusion by SML in the Shearlet Subbands

It is now widely acknowledged that traditional wavelets are not very effective in dealing with multidimensional signals containing distributed discontinuities. Shearlet Transform is a new discrete multiscale directional representation, which combines the power of multiscale methods with a unique ability to capture the geometry of multidimensional data and is optimally efficient in representing images containing edges. In this work, coefficients with greater Sum-Modified-Laplacian are selected to combine images when high-frequency and low-frequency Shearlet subbands of source images are compared. Numerical experiments demonstrate that the method base on Shearlet Transform and Sum-Modified-Laplacian is very competitive and better than other multi-scale geometric analysis tools in multifocus image fusion both in terms of objectives performance and objective criteria. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3365

Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform

A novel image fusion technique based on NSST (non-subsampled shearlet transform) is presented, aiming at resolving the fusion problem of spatially gray-scale visual light and infrared images. NSST, as a new member of MGA (multi-scale geometric analysis) tools, possesses not only flexible direction features and optimal shift-invariance, but much better fusion performance and lower computational costs compared with several current popular MGA tools such as NSCT (non-subsampled contourlet transform). We specifically propose new rules for the fusion of low and high frequency sub-band coefficients of source images in the second step of the NSST-based image fusion algorithm. First, the source images are decomposed into different scales and directions using NSST. Then, the model of region average energy (RAE) is proposed and adopted to fuse the low frequency sub-band coefficients of the gray-scale visual light and infrared images. Third, the model of local directional contrast (LDC) is given and utilized to fuse the corresponding high frequency sub-band coefficients. Finally, the final fused image is obtained by using inverse NSST to all fused sub-images. In order to verify the effectiveness of the proposed technique, several current popular ones are compared over three different publicly available image sets using four evaluation metrics, and the experimental results demonstrate that the proposed technique performs better in both subjective and objective qualities.

A novel multi-modal medical image fusion method based on shift-invariant shearlet transform

Medical image fusion plays an important role in clinical applications, such as image-guided surgery, image-guided radiotherapy, non-invasive diagnosis and treatment planning. Shearlet is a novel multi-scale geometric analysis (MGA) tool proposed recently. In order to overcome the drawback of the shearlet-based fusion methods that the pseudo-Gibbs phenomenon is easily caused around the singularities of the fused image, a new multi-modal medical image fusion method is proposed in shift-invariant shearlet transform domain. First, the original images are decomposed into lowpass sub-bands and highpass sub-bands; then, the lowpass sub-bands and high sub-bands are combined according to the fusion rules, respectively. All the operations are performed in shift-invariant shearlet domain. The final fused image is obtained by directly applying inverse shift-invariant shearlet transform to the fused lowpass sub-bands and highpass sub-bands. Experimental results demonstrate that the proposed method can not only suppress the pseudo-Gibbs phenomenon efficiently, but perform better than the popular wavelet transform-based method, contourlet transform-based method and non-subsampled contourlet transform-based method.

Technique for image fusion between gray-scale visual light and infrared images based on NSST and improved RF

A new technique for image fusion between gray-scale visual light and infrared images based on non-subsampled shearlet transform (NSST) and improved receptive field (RF) is proposed in this paper. As a novel multi-scale geometric analysis tool, NSST can be optimally efficient in representing images containing edges and capturing the geometric features of multidimensional data. As a result, NSST is introduced into the field of image fusion in this paper and utilized to obtain the fused image. The source images could be decomposed in any scale and any direction, so the detailed information can be caught easily. Then, the basic traditional RF model is improved to be improved RF (IRF) which has much fewer parameters and a more effective structure compared with RF. Thirdly, with IRF and the model of local directional contrast (LDC), the fused sub-images can be achieved. Finally, the final fused image can be obtained by using inverse NSST to all fused sub-images. The numerical experiments demonstrate that the new technique presented in this paper is very competitive in image fusion applications in terms of both fusion performance and computational efficiency.

Technique for image fusion based on NSST domain improved fast non-classical RF

In this paper, a novel technique for image fusion based on NSST (Non-Subsampled Shearlet Transform) domain improved fast non-classical RF (Receptive Field) is proposed to resolve the problem of the multi-sensor image fusion. As a novel multi-scale geometric analysis tool, NSST can be optimally efficient in representing images containing edges and capturing the geometric features of multidimensional data. As a result, NSST is introduced into the field of image fusion in this paper and utilized to obtain the final fused image. Multi-scale and multi-directional sparse decompositions of source images are performed by NSST to obtain the main and detailed information. Then, the basic traditional RF model is modified to be an improved fast non-classical RF (IFRF) which has much fewer parameters and a more effective structure compared with RF. Thirdly, with IFRF and the model of local directional contrast (LDC), the fused sub-images can be achieved. Meanwhile, the algorithm for image fusion based on NSST domain improved fast non-classical RF is devised. Finally, the final fused image can be obtained by using inverse NSST to all fused sub-images. The numerical experiments demonstrate that the new technique presented in this paper has not only much better performance, but good running efficiency.

Technique for image fusion based on nonsubsampled shearlet transform and improved pulse-coupled neural network

A new technique for image fusion based on nonsubsampled shearlet transform (NSST) and improved pulse-coupled neural network (PCNN) is proposed. NSST, as a novel multiscale geometric analysis tool, can be optimally efficient in representing images and capturing the geometric features of multidimensional data. As a result, NSST is introduced into the area of image fusion to complete the decompositions of source images in any scale and any direction. Then the basic PCNN model is improved to be improved PCNN (IPCNN), which is more concise and more effective. IPCNN adopts the contrast of each pixel in images as the linking strength β , and the time matrix T of subimages can be obtained via the synchronous pulse-burst property. By using IPCNN, the fused subimages can be achieved. Finally, the final fused image can be obtained by using inverse NSST. The numerical experiments demonstrate that the new technique presented in this paper is competitive in the field of image fusion in terms of both fusion performance and computational efficiency.

BRAIN MR IMAGE CLASSIFICATION USING MULTISCALE GEOMETRIC ANALYSIS OF RIPPLET

We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an e-cient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 £ 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classiflcation accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is e-cient in brain MR image classiflcation.

A Line Enhancement Algorithm for Infrared Image Based on Ridgelet

In order to well detect target’s line character for infrared image, we propose improving line character in image through using ridgelet transform which is a kind of multiscale geometric analysis tool and it is especially suitable for describing the 2-D signals which have linear or super-plane singularities. This paper briefs the principle of ridgelet transform and explains our enhancement algorithm in some detail. Its core consists of the method of tensile scale of coefficient in Radon transform domain, filtering method in processing of one dimensional wavelet transform and the algorithm steps of improving line character. It reconstructs images and compares with the other traditional method as presented and analyzes scale of coefficient and time complexity. Our enhancement method is proved effective preliminarily by experimental results.

Multimodal Medical Image Fusion Using Discrete Ripplet Transform and Intersecting Cortical Model

Image fusion is the process of combining multiple images of the same scene into a single fused image. It preserves the full content information and retains the important features from each of the original images. In this paper, a fusion algorithm based on Discrete Ripplet Transform (DRT) and Intersecting Cortical Model (ICM) is proposed. The registered CT and MRI images are transformed using DRT – a Multiscale Geometric Analysis (MGA) tool. ICM is used to make an intelligent fusion decision to obtain the fusion coefficients in all the subbands of the ripplet transform. The fused image is obtained by applying inverse DRT to the fused coefficients. The proposed algorithm is compared with the existing fusion methods. The evaluation metrics used for comparison are entropy, standard deviation and average gradient. The simulation results show that the proposed algorithm is very effective in fusing multimodal medical images.

MEDICAL IMAGE FUSION BASED ON RIPPLET TRANSFORM TYPE-I

The motivation behind fusing multimodality, multi- resolution images is to create a single image with improved interpretability. In this paper, we propose a novel multimodality Medical Image Fusion (MIF) method, based on Ripplet Transform Type-I (RT) for spatially registered, multi-sensor, multi-resolution medical images. RT is a new Multi-scale Geometric Analysis (MGA) tool, capable of resolving two dimensional (2D) singularities and representing image edges more e-ciently. The source medical images are flrst transformed by discrete RT (DRT). Difierent fusion rules are applied to the difierent subbands of the transformed images. Then inverse DRT (IDRT) is applied to the fused coe-cients to get the fused image. The performance of the proposed scheme is evaluated by various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN) etc. Visual and quantitative analysis shows, that the proposed technique performs better compared to fusion scheme based on Contourlet Transform (CNT).

SAR IMAGE EDGE DETECTION BASED ON IMPROVED WEDGELET TRANSFORM

For SAR images,traditional edge detection methods can hardly extract edges since it is very difficult to balance the noise suppression and the integrity of edges and the veracity of edge position at the same time. However,Wedgelet,which is a multiscale geometric analysis tool,has an advantage of catching linear features. Aiming at further reducing speckle noise in SAR images,a modified cost function of Wedgelet transform was proposed. Besides,proper transform scales were selected in order to highly improve the transform speed without affecting the quality of the approximation. Based on the improved Wedgelet transform,edge detection was preformed. Experimental results show that the proposed method can suppress speckle noise in SAR images successfully and extract edges from SAR images effectively.

Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform

As a novel multiscale geometric analysis tool, contourlet has shown many advantages over the conventional image representation methods. In this paper, a new fusion algorithm for multimodal medical images based on contourlet transform is proposed. All fusion operations are performed in contourlet domain. A novel contourlet contrast measurement is developed, which is proved to be more suitable for human vision system. Other fusion rules like local energy, weighted average and selection are combined with “region” idea for coefficient selection in the lowpass and highpass subbands, which can preserve more details in source images and further improve the quality of fused image. The final fusion image is obtained by directly applying inverse contourlet transform to the fused lowpass and highpass subbands. Extensive fusion experiments have been made on three groups of multimodality CT/MR dataset, both visual and quantitative analysis show that comparing with conventional image fusion algorithms, the proposed approach can provide a more satisfactory fusion outcome.